Methods, systems, and devices for scalable and layered architecture for real-time key performance indicator (kpi) prediction in mobile networks

Abstract

Aspects of the subject disclosure may include, for example, receiving a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network, and obtaining a group of identifiers associated with the mobile network entity. Further embodiments can include obtaining a group of KPIs associated with the mobile network entity based on the group of identifiers, and determining a KPI prediction associated with the mobile network entity based on the group of KPIs. Additional embodiments can include allocating a group of network resources to the mobile network entity based on the KPI prediction. Other embodiments are disclosed.

What is claimed is: 
     
         1 . A device, comprising:
 a processing system including a processor; and   a memory that stores executable instructions that, when executed by the processing system, facilitate performance of operations, the operations comprising:   receiving a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network;   obtaining a group of identifiers associated with the mobile network entity;   obtaining a group of KPIs associated with the mobile network entity based on the group of identifiers, wherein the group of KPIs includes a group of physical uplink shared channel (PUSCH) signal to interference and noise ratio (SINR) indicators, a group of physical uplink control channel (PUCCH) SINR indicators, and a distance between a user end device and a base station;   determining a KPI prediction associated with the mobile network entity based on the group of KPIs; and   allocating a group of network resources to the mobile network entity based on the KPI prediction.   
     
     
         2 . The device of  claim 1 , wherein the KPI prediction is based on a short-term KPI prediction. 
     
     
         3 . The device of  claim 1 , wherein the KPI prediction is based on a channel quality indicator (CQI) prediction. 
     
     
         4 . The device of  claim 1 , wherein the KPI prediction is based on a long-term KPI prediction. 
     
     
         5 . The device of  claim 1 , wherein the KPI prediction is based on a cell-based KPI prediction. 
     
     
         6 . The device of  claim 1 , wherein the group of identifiers comprises a group of International Mobile Subscriber Identities (IMSIs), group of international mobile equipment identities (IMEIs), a group of physical cell identifiers, a group of extended cell global identifiers (ECGIs), or any combination thereof. 
     
     
         7 . The device of  claim 1 , wherein the group of KPIs comprises a group of channel quality indicators (CQIs), a group of signal strength indicators, a group of reference signal receive power (RSRP) indicators, a group of reference signal receive quality (RSRQ) indicators, a group of signal to noise ratio (SNR) indicators, or any combination thereof. 
     
     
         8 . The device of  claim 1 , wherein the determining of the KPI prediction comprises determining the KPI prediction based on the group of KPIs utilizing one or more of a group of machine learning models, a group of artificial intelligence models, or a group of time series models. 
     
     
         9 . The device of  claim 1 , wherein the KPI prediction applies to at least a smart phone and an Internet of Things (IoT) device of the mobile network. 
     
     
         10 . The device of  claim 1 , wherein the KPI prediction applies to a downlink throughput and an uplink throughput. 
     
     
         11 . The device of  claim 1 , wherein the KPI prediction applies to a cluster of cells. 
     
     
         12 . The device of  claim 11 , wherein the cluster of cells corresponds to a cluster of base stations. 
     
     
         13 . A non-transitory, machine-readable medium, comprising executable instructions that, when executed by a processing system including a processor, facilitate performance of operations, the operations comprising:
 receiving a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network;   obtaining a group of identifiers associated with the mobile network entity;   obtaining a group of KPIs associated with the mobile network entity based on the group of identifiers, wherein the group of KPIs includes a physical uplink shared channel (PUSCH) signal to interference and noise ratio (SINR) indicator, a physical uplink control channel (PUCCH) SINR indicator, and a distance between a user end device and a base station;   determining a KPI prediction associated with the mobile network entity based on the group of KPIs utilizing least square estimation; and   allocating a group of network resources to the mobile network entity based on the KPI prediction.   
     
     
         14 . The non-transitory, machine-readable medium of  claim 13 , wherein the determining of the KPI prediction comprises combining a short-term KPI prediction, a long-term KPI prediction, and a cell-based KPI prediction. 
     
     
         15 . The non-transitory, machine-readable medium of  claim 13 , wherein the group of identifiers comprises a group of International Mobile Subscriber Identities (IMSIs), a group of international mobile equipment identities (IMEIs), a group of physical cell identifiers, group of extended cell global identifiers (ECGIs), or any combination thereof. 
     
     
         16 . The non-transitory, machine-readable medium of  claim 13 , wherein the group of KPIs comprises a group of channel quality indicators (CQIs). 
     
     
         17 . The non-transitory, machine-readable medium of  claim 13 , wherein the group of KPIs comprises a group of signal strength indicators, a group of reference signal receive power (RSRP) indicators, and a group of reference signal receive quality (RSRQ) indicators. 
     
     
         18 . The non-transitory, machine-readable medium of  claim 13 , wherein the determining of the KPI prediction comprises determining the KPI prediction based on the group of KPIs utilizing a group of machine learning models, a group of artificial intelligence models, and a group of time series models. 
     
     
         19 . A method, comprising:
 receiving, by a processing system including a processor, a request from a mobile network entity for a key performance indicator (KPI) prediction over a portion of a mobile network;   obtaining, by the processing system, a group of identifiers associated with the mobile network entity;   obtaining, by the processing system, a group of KPIs associated with the mobile network entity based on the group of identifiers, wherein the group of KPIs includes a group of physical uplink shared channel (PUSCH) signal to interference and noise ratio (SINR) indicators, a group of physical uplink control channel (PUCCH) SINR indicators, and a distance between a user end device and a network equipment;   determining, by the processing system, a KPI prediction associated with the mobile network entity based on the group of KPIs; and   allocating, by the processing system, a group of network resources to the mobile network entity based on the KPI prediction.   
     
     
         20 . The method of  claim 19 , wherein the determining of the KPI prediction comprises determining the KPI prediction based on a cell-based KPI prediction utilizing one or more of a group of machine learning models, a group of artificial intelligence models, or a group of time series models.